Multimodal Ultrasound/Optical Method for Basal Cell Carcinoma Recognition Using Machine Learning
Abstract
The development of noninvasive methods for diagnosing skin diseases is a major problem in modern medicine. This paper presents an advanced multimodal approach that integrates Optical Coherence Tomography, Diffuse Reflectance Spectroscopy, and High Frequency Ultrasound data. Image processing, spectral and texture analysis, and machine learning techniques, including gradient boosting and fusion algorithms for unimodal classifiers, were used to analyze the data. The developed multimodal model provides high accuracy (96%) in differentiating benign and malignant neoplasms, demonstrating significant potential for clinical diagnostics. These results highlight the importance of integrating data from multiple modalities to improve diagnostic accuracy and reliability for skin neoplasms.
Fields of Study & Subjects
Cutaneous Melanoma Detection and Management
Nonmelanoma Skin Cancer Studies
AI in cancer detection
Publication details
- Journal / source
- IEEE Journal of Selected Topics in Quantum Electronics
- Volume
- 32
- Issue
- 4: Adv. Biophoton. in Emerg.
- Pages
- 1–9
- Publication date
- 4 March 2026
- Language
- EN